Making Customer Insight Analytics Actionable

Like most large commercial banks, I’m sure that my bank views my checking account for exactly what it is on its balance sheet, a liability. To be fair, I treat it as my personal controlled disbursement account. Money is directly deposited semi-monthly, and evaporates just as fast to cover my mortgage, bill payments along with the occasional check or ATM withdrawal. For savings and the bulk of my banking needs, I rely on credit unions, which after the Global Financial Crisis (GFC) have come to fulfill the role of trusted financial advisors to millions.  I also rely on a wealth management firm, which provides a greater breadth of retirement planning services, and, once again, fulfills the role of trusted financial advisor.

Consumer banking has become a very tough economic proposition for most large commercial banks. In the aftermath of the GFC, the Consumer Financial Protection Bureau (CFPB) pressured banks’ overdraft revenue streams while Dodd-Frank’s Durbin Amendment limited their massive debit card interchange income. To add insult to injury, as consumers and small businesses deleveraged during the GFC and their deposit balances increased, FDIC fees on insured deposits rose. Once viewed as a stable source of low-cost funding and driving bank valuations, excess retail deposits have become a true liability for many banks by pressuring their margins. Look at the industry’s Return on Equity pre- and post-GFC to further illustrate this point.

For most banks, the average consumer checking account is a loss leader with servicing costs far exceeding both fees collected and the value of account balances. Clearly most banks need to overhaul their consumer banking value propositions to drive profitability with marginal customers like me to help grow their wallet share via cross/up-selling higher margin products and services to potentially profitable customers.

After years of investing in a hodgepodge of costly data warehouses and other business intelligence capabilities to figure this out, many banks are just beginning to piece together that elusive 360-degree customer view, mostly on the backend of their processes. That is, my bank now knows that I have a first mortgage and dormant brokerage account with them, and hopefully it’ll figure out what to do with this valuable information. I can’t be alone in thinking, “Why doesn’t my bank get more proactive in trying to expand my relationship?” With the right analytics, banks should be able to leverage the reams of data that they have about their customers along with some externally available information (i.e. big data), and make compelling offers and improve customer treatment. Both are necessary to deepen relationships and enhance profitability.

Today’s banking customers have very different demands than those 10 years ago. Spoiled by other industries, especially non-bank financial services providers (e.g. PayPal, Square, Amazon, etc.), they now demand that their banks know them, wow them, and empower them. That is, customers favor solutions instead of products, and they expect to own the decision making process.

For example, my cellular telephone carrier instantly alerts me whenever I get near my monthly data allowance. To avoid incurring substantial overage fees, it offers me several options during that billing cycle which can take effect retroactively: I can choose to (a) ease up on usage and stay within the data limit, (b) purchase another gigabyte of data, or (c) upgrade to a more expensive plan with a higher data allowance. Why don’t banks borrow from the cellular industry and alert and/or upsell their customers exceeding monthly ATM withdrawals with premium banking packages or bundles of additional ATM transactions? To turn a negative situation positive, why don’t banks send real-time alerts and/or retroactively position overdraft-lines-of-credit to high-value and potentially high-value customers about to bounce checks? Since many banks have difficulty in identifying their profitable, let alone potentially profitable customers, blanket treatment policies prevail.

Good customer insight analytics can aid banks in making sound decisions on treating customers. To manage customer lifecycles, banks should consider behavioral analyses, life events, lifetime value, broad financial views and previous successful experiences as part of their customer insight programs. For example, customer insight propensity models can guide a bank’s understanding of the likelihood that their customers will embrace additional products or utilize alternate channels. Customer lifetime value analytics can help a bank make decisions based on future potential of a customer’s relationship. For example, a customer with a high potential lifetime value calling in an address change out of the bank’s market area could be offered a special retention package, complete with included off-us ATM transactions, to prevent future, undesirable attrition.

The first step of embarking on an effective customer insight program begins with the data. Silo’d systems-of-record and numerous, equally silo’d, data warehouses and data marts are often strung together with Excel to perform a bank’s financial, regulatory and management reporting. By standardizing on a unified data model, banks are better positioned to run their customer insight models accurately, uniformly, and aligned with their other reporting. Once the bank has confidence in its data, then various statistical and behavioral models can be employed to produce actionable insight that can help banks deliver a better customer experience, deepen relationships and enhance profitability.

Is your bank effectively leveraging its data assets to differentiate customer experience? I’d be interested in hearing how you are using data and analytics to increase share-of-wallet and enhance relationship profitability.